Back in 2005 at Goldmans, the technology we were developing was seen to be cutting edge. Goldman had invested billions of dollars in creating a central repository of all its data, called SecDB. This was before ‘the cloud’ and all data was hosted in their own data centres. Fast forward to 2013, when I was in Silicon Valley and building machine learning tools to process the Twitter feed, AWS cloud computing was the ‘new tech on the block’, and Naive Bayes was the best classification model available to make sense of vast amounts of text.
In 2025, compute and chip speed is driving unbelievable innovation with generative AI.
The capabilities of modern processing hardware, like Nvidia’s latest GPUs, have shattered old records, enabling machines to process massive data sets in milliseconds, performing trillions of operations per second. Large Language Models (LLMs) like GPT-4 and its successors have redefined what’s possible, quickly bringing advanced, human-like interactions into the mainstream.
Looking back over 20 years underscores a critical reality: technology is transforming so rapidly that it can feel impossible to keep up.
Is your data AI-ready?
Can you leverage the vast resources of open-source models now available?
Is your data accessible and machine-readable, ready to drive AI insights?
Do you even know where to begin?
If you’re in asset management, these aren’t just questions—they’re imperatives. The competitive advantage of firms that are AI-ready is growing exponentially, as they can leverage cutting-edge insights faster and more efficiently than those mired in outdated processes.
AI-Ready Data in Your Organization is Closer Than You Think
Achieving AI readiness isn’t an insurmountable challenge. With the right approach, your organization can harness data as a true strategic asset, building a foundation for sustainable growth and resilience in an ever-evolving market. Here’s how:
1. Set Up Cloud-Based Infrastructure: Moving to the cloud unlocks scale and flexibility that simply isn’t possible with on-premises solutions. It allows for agile adjustments, scalability, and remote accessibility, crucial for data-driven insights. This might sound easier said than done, but advances in DevOps engineering and tools provided by AWS, Azure, Google and Ali Cloud make access easy and affordable.
Centralise Your Key Data into a Database: A centralised data repository is essential for consolidating disparate data sources, whether they’re from third-party vendors, internal teams, or external feeds. This single source of truth is the foundation of any successful AI initiative.
Target Bottlenecks and Manual Processes for Automation: Identify and streamline inefficient processes that are important, but repetitive and manual. Whether it’s automating data ingestion or streamlining analysis workflows, each optimization frees up time and resources for high-value activities.
Integrate Readily Available GenAI Models and APIs: There’s a world of open-source GenAI models and APIs available to accelerate your AI initiatives. Once your data is centralized and machine-readable, integrating these models can provide immediate insights and decision-making support, elevating your firm’s analytical capabilities.
Given the rate of change in technology – being AI-ready is no longer optional.
The path is clear, and the tools are available.
Are you ready to take the first step?
If you want help making this a reality, drop me a note: steve@insig.ai.
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